@dataclass-style structured configs to @attrs-style

examples/new_actions.py

@@ -12,7 +12,7 @@
 We will use the strafe action outline in the habitat_sim example
 """
 
-import attr
+import attrs
 import numpy as np
 
 import habitat
@@ -25,7 +25,7 @@
 from habitat.tasks.nav.nav import SimulatorTaskAction
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class NoisyStrafeActuationSpec:
     move_amount: float
     # Classic strafing is to move perpendicular (90 deg) to the forward direction

examples/register_new_sensors_and_measures.py

@@ -4,9 +4,9 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from dataclasses import dataclass
 from typing import Any
 
+import attrs
 import numpy as np
 from gym import spaces
 from omegaconf import MISSING
@@ -46,7 +46,7 @@ def update_metric(self, *args: Any, episode, action, **kwargs: Any):
 
 
 # define a configuration for this new measure
-@dataclass
+@attrs.define(auto_attribs=True)
 class EpisodeInfoExampleConfig(MeasurementConfig):
     # Note that typing is required on all fields
     type: str = "EpisodeInfoExample"
@@ -89,7 +89,7 @@ def get_observation(
 
 
 # define a configuration for this new sensor
-@dataclass
+@attrs.define(auto_attribs=True)
 class AgentPositionSensorConfig(LabSensorConfig):
     # Note that typing is required on all fields
     type: str = "my_supercool_sensor"

examples/tutorials/colabs/Habitat2_Quickstart.ipynb

@@ -17,7 +17,7 @@
    "outputs": [],
    "source": [
     "# Play a teaser video\n",
-    "from dataclasses import dataclass\n",
+    "import attrs\n",
     "\n",
     "from habitat.config.default import get_agent_config\n",
     "from habitat.config.default_structured_configs import (\n",
@@ -354,12 +354,12 @@
     "        self._metric = abs_targ_obj_idx == self._sim.grasp_mgr.snap_idx\n",
     "\n",
     "\n",
-    "@dataclass\n",
+    "@attrs.define(auto_attribs=True)\n",
     "class DistanceToTargetObjectMeasurementConfig(MeasurementConfig):\n",
     "    type: str = \"DistanceToTargetObject\"\n",
     "\n",
     "\n",
-    "@dataclass\n",
+    "@attrs.define(auto_attribs=True)\n",
     "class NavPickRewardMeasurementConfig(MeasurementConfig):\n",
     "    type: str = \"NavPickReward\"\n",
     "    scaling_factor: float = 0.1\n",
@@ -370,7 +370,7 @@
     "    force_end_pen: float = 10.0\n",
     "\n",
     "\n",
-    "@dataclass\n",
+    "@attrs.define(auto_attribs=True)\n",
     "class NavPickSuccessMeasurementConfig(MeasurementConfig):\n",
     "    type: str = \"NavPickSuccess\"\n",
     "\n",

examples/tutorials/nb_python/Habitat2_Quickstart.py

@@ -27,7 +27,7 @@
 
 # %%
 # Play a teaser video
-from dataclasses import dataclass
+import attrs
 
 from habitat.config.default import get_agent_config
 from habitat.config.default_structured_configs import (
@@ -309,12 +309,12 @@ def update_metric(self, *args, episode, task, observations, **kwargs):
         self._metric = abs_targ_obj_idx == self._sim.grasp_mgr.snap_idx
 
 
-@dataclass
+@attrs.define(auto_attribs=True)
 class DistanceToTargetObjectMeasurementConfig(MeasurementConfig):
     type: str = "DistanceToTargetObject"
 
 
-@dataclass
+@attrs.define(auto_attribs=True)
 class NavPickRewardMeasurementConfig(MeasurementConfig):
     type: str = "NavPickReward"
     scaling_factor: float = 0.1
@@ -325,7 +325,7 @@ class NavPickRewardMeasurementConfig(MeasurementConfig):
     force_end_pen: float = 10.0
 
 
-@dataclass
+@attrs.define(auto_attribs=True)
 class NavPickSuccessMeasurementConfig(MeasurementConfig):
     type: str = "NavPickSuccess"
 

habitat-baselines/habitat_baselines/agents/ppo_agents.py

@@ -7,9 +7,9 @@
 
 import argparse
 import random
-from dataclasses import dataclass
 from typing import Dict, Optional
 
+import attrs
 import numpy as np
 import torch
 from gym.spaces import Box
@@ -24,7 +24,7 @@
 from habitat_baselines.utils.common import batch_obs
 
 
-@dataclass
+@attrs.define(auto_attribs=True)
 class PPOAgentConfig:
     INPUT_TYPE: str = "rgb"
     MODEL_PATH: str = "data/checkpoints/gibson-rgb-best.pth"

habitat-baselines/habitat_baselines/common/windowed_running_mean.py

@@ -8,18 +8,18 @@
 import numbers
 from typing import Optional, Sequence, Union
 
-import attr
+import attrs
 import numpy as np
 
 
-@attr.s(auto_attribs=True, slots=True, repr=False)
+@attrs.define(auto_attribs=True, repr=False)
 class WindowedRunningMean:
     r"""Efficient implementation of a windowed running mean. Supports an infinite window"""
     window_size: Union[int, float]
-    _sum: float = attr.ib(0.0, init=False)
-    _count: int = attr.ib(0, init=False)
-    _ptr: int = attr.ib(0, init=False)
-    _buffer: Optional[np.ndarray] = attr.ib(None, init=False)
+    _sum: float = attrs.field(default=0.0, init=False)
+    _count: int = attrs.field(default=0, init=False)
+    _ptr: int = attrs.field(default=0, init=False)
+    _buffer: Optional[np.ndarray] = attrs.field(default=None, init=False)
 
     def __attrs_post_init__(self):
         if not self.infinite_window:

habitat-baselines/habitat_baselines/config/default_structured_configs.py

@@ -6,7 +6,7 @@
 
 from typing import Any, Dict, List, Optional, Tuple
 
-import attr
+import attrs
 from hydra.core.config_store import ConfigStore
 from omegaconf import MISSING
 
@@ -15,12 +15,12 @@
 cs = ConfigStore.instance()
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class HabitatBaselinesBaseConfig:
     pass
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class WBConfig(HabitatBaselinesBaseConfig):
     """Weights and Biases config"""
 
@@ -35,7 +35,7 @@ class WBConfig(HabitatBaselinesBaseConfig):
     run_name: str = ""
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class EvalConfig(HabitatBaselinesBaseConfig):
     # The split to evaluate on
     split: str = "val"
@@ -50,7 +50,7 @@ class EvalConfig(HabitatBaselinesBaseConfig):
     extra_sim_sensors: Dict[str, SimulatorSensorConfig] = dict()
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class PreemptionConfig(HabitatBaselinesBaseConfig):
     # Append the slurm job ID to the resume state filename if running
     # a slurm job. This is useful when you want to have things from a different
@@ -63,7 +63,7 @@ class PreemptionConfig(HabitatBaselinesBaseConfig):
     save_state_batch_only: bool = False
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class ActionDistributionConfig(HabitatBaselinesBaseConfig):
     use_log_std: bool = True
     use_softplus: bool = False
@@ -82,12 +82,12 @@ class ActionDistributionConfig(HabitatBaselinesBaseConfig):
     scheduled_std: bool = False
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class ObsTransformConfig(HabitatBaselinesBaseConfig):
     type: str = MISSING
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class CenterCropperConfig(ObsTransformConfig):
     type: str = "CenterCropper"
     height: int = 256
@@ -108,7 +108,7 @@ class CenterCropperConfig(ObsTransformConfig):
 )
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class ResizeShortestEdgeConfig(ObsTransformConfig):
     type: str = "ResizeShortestEdge"
     size: int = 256
@@ -129,7 +129,7 @@ class ResizeShortestEdgeConfig(ObsTransformConfig):
 )
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class Cube2EqConfig(ObsTransformConfig):
     type: str = "CubeMap2Equirect"
     height: int = 256
@@ -152,7 +152,7 @@ class Cube2EqConfig(ObsTransformConfig):
 )
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class Cube2FishConfig(ObsTransformConfig):
     type: str = "CubeMap2Fisheye"
     height: int = 256
@@ -177,7 +177,7 @@ class Cube2FishConfig(ObsTransformConfig):
 )
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class AddVirtualKeysConfig(ObsTransformConfig):
     type: str = "AddVirtualKeys"
     virtual_keys: Dict[str, int] = dict()
@@ -191,7 +191,7 @@ class AddVirtualKeysConfig(ObsTransformConfig):
 )
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class Eq2CubeConfig(ObsTransformConfig):
     type: str = "Equirect2CubeMap"
     height: int = 256
@@ -214,7 +214,7 @@ class Eq2CubeConfig(ObsTransformConfig):
 )
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class HrlDefinedSkillConfig(HabitatBaselinesBaseConfig):
     """
     Defines a low-level skill to be used in the hierarchical policy.
@@ -248,14 +248,14 @@ class HrlDefinedSkillConfig(HabitatBaselinesBaseConfig):
     pddl_action_names: Optional[List[str]] = None
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class HierarchicalPolicyConfig(HabitatBaselinesBaseConfig):
     high_level_policy: Dict[str, Any] = MISSING
     defined_skills: Dict[str, HrlDefinedSkillConfig] = dict()
     use_skills: Dict[str, str] = dict()
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class PolicyConfig(HabitatBaselinesBaseConfig):
     name: str = "PointNavResNetPolicy"
     action_distribution_type: str = "categorical"  # or 'gaussian'
@@ -266,7 +266,7 @@ class PolicyConfig(HabitatBaselinesBaseConfig):
     hierarchical_policy: HierarchicalPolicyConfig = MISSING
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class PPOConfig(HabitatBaselinesBaseConfig):
     """Proximal policy optimization config"""
 
@@ -297,7 +297,7 @@ class PPOConfig(HabitatBaselinesBaseConfig):
     use_double_buffered_sampler: bool = False
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class VERConfig(HabitatBaselinesBaseConfig):
     """Variable experience rollout config"""
 
@@ -306,12 +306,12 @@ class VERConfig(HabitatBaselinesBaseConfig):
     overlap_rollouts_and_learn: bool = False
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class AuxLossConfig(HabitatBaselinesBaseConfig):
     pass
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class CPCALossConfig(AuxLossConfig):
     """Action-conditional contrastive predictive coding loss"""
 
@@ -321,7 +321,7 @@ class CPCALossConfig(AuxLossConfig):
     loss_scale: float = 0.1
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class DDPPOConfig(HabitatBaselinesBaseConfig):
     """Decentralized distributed proximal policy optimization config"""
 
@@ -344,7 +344,7 @@ class DDPPOConfig(HabitatBaselinesBaseConfig):
     force_distributed: bool = False
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class RLConfig(HabitatBaselinesBaseConfig):
     """Reinforcement learning config"""
 
@@ -356,13 +356,13 @@ class RLConfig(HabitatBaselinesBaseConfig):
     auxiliary_losses: Dict[str, AuxLossConfig] = dict()
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class ProfilingConfig(HabitatBaselinesBaseConfig):
     capture_start_step: int = -1
     num_steps_to_capture: int = -1
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class HabitatBaselinesConfig(HabitatBaselinesBaseConfig):
     # task config can be a list of configs like "A.yaml,B.yaml"
     # If habitat_baselines.evaluate is true, the run will be in evaluation mode
@@ -411,17 +411,17 @@ class HabitatBaselinesConfig(HabitatBaselinesBaseConfig):
     profiling: ProfilingConfig = ProfilingConfig()
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class HabitatBaselinesRLConfig(HabitatBaselinesConfig):
     rl: RLConfig = RLConfig()
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class HabitatBaselinesILConfig(HabitatBaselinesConfig):
     il: Dict[str, Any] = dict()
 
 
-@attr.s(auto_attribs=True, slots=True)
+@attrs.define(auto_attribs=True)
 class HabitatBaselinesSPAConfig(HabitatBaselinesConfig):
     sense_plan_act: Any = MISSING